PDF(Pubmed)
Abstract：
UNASSIGNED: Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others.
UNASSIGNED: In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning.
UNASSIGNED: The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.
UNASSIGNED: In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning.
UNASSIGNED: The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.
摘要：
■自动驾驶是一个不断发展的研究领域，为科学带来了好处，经济,和社会。虽然这方面有很多研究,目前还没有完全自主的车辆，特别是,用于越野导航。自动驾驶汽车（AV）导航是一个复杂的过程，基于多种技术和算法的数据采集的应用，管理和理解。特别是,自动驾驶辅助系统支持传感和地形感知等关键功能，实时车辆映射和定位，路径预测和驱动，通信和安全措施，在其他人中。
■在这项工作中，提出了一种结合视频帧语义分割和后续实时路线规划的越野环境中车辆自动驾驶的原始方法。为了检查提案的相关性，设计了一个面向资源受限设备的越野场景辅助驾驶的模块化框架。在场景感知模块中,深度神经网络用于分割从相机获得的红-绿-蓝(RGB)图像。第二可遍历性模块融合光检测和测距(LiDAR)点云与分割的结果以创建二元占用网格地图,以在自主导航期间提供场景理解。最后,最后一个模块，基于快速探索随机树(RRT)算法，预测一条路。弗赖堡森林数据集（FFD）和RELLIS-3D数据集用于评估所提出方法的性能。本文的理论贡献包括适用于越野驾驶场景的原始图像语义分割方法，以及使最短路线搜索A*和RRT算法适应AV路径规划。
■报告的结果非常有希望，与以前报告的解决方案相比，显示出几个优点。FFD的分割精度为85.9％，RELLIS-3D的分割精度为79.5％，包括最常见的语义类。与其他方法相比，所提出的方法在路径规划的计算时间方面更快。
■在这项工作中，提出了一种结合视频帧语义分割和后续实时路线规划的越野环境中车辆自动驾驶的原始方法。为了检查提案的相关性，设计了一个面向资源受限设备的越野场景辅助驾驶的模块化框架。在场景感知模块中,深度神经网络用于分割从相机获得的红-绿-蓝(RGB)图像。第二可遍历性模块融合光检测和测距(LiDAR)点云与分割的结果以创建二元占用网格地图,以在自主导航期间提供场景理解。最后,最后一个模块，基于快速探索随机树(RRT)算法，预测一条路。弗赖堡森林数据集（FFD）和RELLIS-3D数据集用于评估所提出方法的性能。本文的理论贡献包括适用于越野驾驶场景的原始图像语义分割方法，以及使最短路线搜索A*和RRT算法适应AV路径规划。
■报告的结果非常有希望，与以前报告的解决方案相比，显示出几个优点。FFD的分割精度为85.9％，RELLIS-3D的分割精度为79.5％，包括最常见的语义类。与其他方法相比，所提出的方法在路径规划的计算时间方面更快。
